{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Results From a Single File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to do math\n",
    "import numpy as np\n",
    "\n",
    "# to draw plots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to import csv files\n",
    "import pandas as pd\n",
    "\n",
    "# to manipulate path\n",
    "import os.path\n",
    "\n",
    "# report error\n",
    "import sys\n",
    "\n",
    "# make sure that the graphs appear directly on this page\n",
    "%matplotlib inline\n",
    "\n",
    "# to produce cosmetic print in markdown format\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "# cosmetic print for numpy\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TODO: fill those variable to your files\n",
    "env_name = 'ETH'\n",
    "results_paths = ['../../../../data/pointmatcher_eval/eth_besl92.csv',\n",
    "                 '../../../../data/pointmatcher_eval/eth_chen91.csv', \n",
    "                ]\n",
    "\n",
    "solution_names = ['point-to-point',\n",
    "                  'point-to-plane',\n",
    "                 ]\n",
    "\n",
    "protocol_path = '../../../../data/pointmatcher_eval/eth_protocol.csv'\n",
    "validation_path = '../../../../data/pointmatcher_eval/eth_validation.csv'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Useful Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_file(data_path):\n",
    "\n",
    "    if not os.path.isfile(protocol_path):\n",
    "        display(Markdown(\"**Error**: Could not find the file _%s_.\"%data_path))\n",
    "        sys.exit()\n",
    "    \n",
    "    data = pd.read_csv(data_path)\n",
    "    # Parse header\n",
    "    nb_rows = len(data)\n",
    "    id_T = np.identity(4)\n",
    "    extra_headers = []\n",
    "    id_extra_header = []\n",
    "    for col, label in enumerate(data.columns.values):\n",
    "        label = label.strip()\n",
    "        if(label[0] == 'T'):\n",
    "            i = int(label[1])\n",
    "            j = int(label[2])\n",
    "            id_T[i,j] = col\n",
    "        elif(label[1] == 'T'):\n",
    "            i = int(label[2])\n",
    "            j = int(label[3])\n",
    "            id_T[i,j] = col\n",
    "        else:\n",
    "            extra_headers.append(label)\n",
    "            id_extra_header.append(col)\n",
    "\n",
    "    # Load data\n",
    "    new_header = extra_headers\n",
    "    new_header.append('T')\n",
    "    data_out = pd.DataFrame(index=np.arange(nb_rows), columns=new_header)\n",
    "\n",
    "    for row in np.arange(nb_rows):\n",
    "        for label, id_head in zip(extra_headers, id_extra_header):\n",
    "            data_out.iloc[row][label] = data.iloc[row, id_head]\n",
    "\n",
    "        T = np.identity(4)\n",
    "        for index, id_head in np.ndenumerate(id_T):\n",
    "            T[index] = data.iloc[row, int(id_head)]\n",
    "\n",
    "        data_out.iloc[row]['T'] = T\n",
    "\n",
    "    if 'time' in data_out.columns:\n",
    "        data_out['time'] = data_out['time'].astype(float, copy=False)\n",
    "    \n",
    "    return data_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load all files for a given environment\n",
    "\n",
    "protocol_data = load_file(protocol_path)\n",
    "validation_data = load_file(validation_path)\n",
    "    \n",
    "results_data_list = []\n",
    "for results_path in results_paths:\n",
    "    \n",
    "    results_data = load_file(results_path)\n",
    "    results_data_list.append(results_data)\n",
    "    \n",
    "    if(not(len(results_data) == len(protocol_data) == len(validation_data))):\n",
    "        display(Markdown(\"**Error**: number of rows are not the same in each file.\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">point-to-plane</th>\n",
       "      <th colspan=\"4\" halign=\"left\">point-to-point</th>\n",
       "      <th colspan=\"3\" halign=\"left\">protocol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">validation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>error_rot</th>\n",
       "      <th>error_trans</th>\n",
       "      <th>time</th>\n",
       "      <th>T</th>\n",
       "      <th>error_rot</th>\n",
       "      <th>error_trans</th>\n",
       "      <th>time</th>\n",
       "      <th>T</th>\n",
       "      <th>reading</th>\n",
       "      <th>reference</th>\n",
       "      <th>T</th>\n",
       "      <th>overlap_ratio</th>\n",
       "      <th>perturbation_rot</th>\n",
       "      <th>perturbation_trans</th>\n",
       "      <th>perturbation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[0.999548, -0.00575572, 0.0295302, 14.87], [-...</td>\n",
       "      <td>1.624117</td>\n",
       "      <td>3.767078</td>\n",
       "      <td>3.37804</td>\n",
       "      <td>[[0.62264, 0.767392, 0.153118, 13.5807], [-0.1...</td>\n",
       "      <td>1.702717</td>\n",
       "      <td>10.283134</td>\n",
       "      <td>2.33435</td>\n",
       "      <td>[[0.951999, 0.225383, 0.207126, 13.808709], [-...</td>\n",
       "      <td>Hokuyo_32.csv</td>\n",
       "      <td>Hokuyo_12.csv</td>\n",
       "      <td>[[0.999736, 0.02279, 0.003118, 14.677482], [-0...</td>\n",
       "      <td>0.359082</td>\n",
       "      <td>0.576353</td>\n",
       "      <td>0.250124</td>\n",
       "      <td>easyPoses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      point-to-plane                        \\\n",
       "                                                   T error_rot error_trans   \n",
       "0  [[0.999548, -0.00575572, 0.0295302, 14.87], [-...  1.624117    3.767078   \n",
       "\n",
       "                                               point-to-point            \\\n",
       "      time                                                  T error_rot   \n",
       "0  3.37804  [[0.62264, 0.767392, 0.153118, 13.5807], [-0.1...  1.702717   \n",
       "\n",
       "                                                                 protocol  \\\n",
       "  error_trans     time                                                  T   \n",
       "0   10.283134  2.33435  [[0.951999, 0.225383, 0.207126, 13.808709], [-...   \n",
       "\n",
       "                                  \\\n",
       "          reading      reference   \n",
       "0   Hokuyo_32.csv  Hokuyo_12.csv   \n",
       "\n",
       "                                          validation                \\\n",
       "                                                   T overlap_ratio   \n",
       "0  [[0.999736, 0.02279, 0.003118, 14.677482], [-0...      0.359082   \n",
       "\n",
       "                                                         \n",
       "  perturbation_rot perturbation_trans perturbation_type  \n",
       "0         0.576353           0.250124         easyPoses  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def perturbation(row):\n",
    "    delta_T = row.protocol['T'].dot(np.linalg.inv(row.validation['T']))\n",
    "    p_trans = np.linalg.norm(delta_T[0:3, 3])\n",
    "    rod = (np.trace(delta_T)/2.) - 1.\n",
    "    # handle value close to -1 or 1\n",
    "    rod = np.min([rod, 1.])\n",
    "    rod = np.max([rod, -1.])\n",
    "    p_rot = np.arccos(rod)\n",
    "    return pd.Series({('validation', 'perturbation_trans'):p_trans, ('validation', 'perturbation_rot'):p_rot})\n",
    "\n",
    "def error(row):\n",
    "    out = []\n",
    "    for name in solution_names:\n",
    "        error_T = row[name, 'T'].dot(np.linalg.inv(row.validation['T']))\n",
    "        e_trans = np.linalg.norm(error_T[0:3, 3])\n",
    "        rod = (np.trace(error_T)/2.) - 1.\n",
    "        # handle value close to -1 or 1\n",
    "        rod = np.min([rod, 1.])\n",
    "        rod = np.max([rod, -1.])\n",
    "        e_rot = np.arccos(rod)\n",
    "        out.append(pd.Series({(name, 'error_trans'):e_trans, (name, 'error_rot'):e_rot}))\n",
    "    return pd.concat(out, axis=0)\n",
    "\n",
    "# combine to one table\n",
    "plot_data = pd.concat(([protocol_data, validation_data] + results_data_list), \n",
    "                      axis=1, keys=(['protocol', 'validation'] + solution_names))\n",
    "\n",
    "# Compute the level of perturbations\n",
    "plot_data = pd.concat([plot_data, plot_data.apply(perturbation, axis=1)], axis=1)\n",
    "\n",
    "# Compute the errors\n",
    "plot_data = pd.concat([plot_data, plot_data.apply(error, axis=1)], axis=1)\n",
    "\n",
    "# display the table\n",
    "plot_data.sort_index(axis='columns', inplace=True)\n",
    "plot_data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OBQAAIDnKBTNQQqlHCUpox5JKdkrIPYiB3BMP+QfojtDlgtEtLqaOAECJKBIAAGAyIQZZ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e3845a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxprops = dict(linestyle='-', linewidth=1, color='blue')\n",
    "medianprops = dict(linestyle='-', linewidth=5, color='red')\n",
    "whiskerprops = dict(linestyle='--', linewidth=1, color='black')\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "plot_data.boxplot([('point-to-point', 'error_trans'), ('point-to-plane', 'error_trans')], \n",
    "                  ax=ax1, return_type=None,\n",
    "                  whis=[0,100], medianprops=medianprops, boxprops=boxprops, whiskerprops=whiskerprops);\n",
    "ax1.set_xticklabels(solution_names)\n",
    "ax1.set_title('$\\it{' + env_name + '}$', fontsize=20)\n",
    "ax1.set_ylabel('Error on translation (m)')\n",
    "ax1.set_yscale('log')\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "plot_data.boxplot([('point-to-point', 'error_rot'), ('point-to-plane', 'error_rot')], \n",
    "                  ax=ax2, return_type=None,\n",
    "                  whis=[0,100], medianprops=medianprops, boxprops=boxprops, whiskerprops=whiskerprops);\n",
    "ax2.set_xticklabels(solution_names)\n",
    "ax2.set_title('$\\it{' + env_name + '}$', fontsize=20)\n",
    "ax2.set_ylabel('Error on rotation (rad)')\n",
    "ax2.set_yscale('log')\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "plot_data.boxplot([('point-to-point', 'time'), ('point-to-plane', 'time')], \n",
    "                  ax=ax3, return_type=None,\n",
    "                  whis=[0,100], medianprops=medianprops, boxprops=boxprops, whiskerprops=whiskerprops);\n",
    "ax3.set_xticklabels(solution_names)\n",
    "ax3.set_title('$\\it{' + env_name + '}$', fontsize=20)\n",
    "ax3.set_ylabel('Time (sec)')\n",
    "ax3.set_yscale('log')\n",
    "\n",
    "fig.tight_layout(w_pad=2.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fOoSUDlqbSkRERCRQ0tLSPMcZGRlkZ2eTk5PjSZoAYmJiSE5OZtu2baSnp9ep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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e3983cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "for name in solution_names:\n",
    "    data = plot_data[name].error_trans.sort(inplace=False).values\n",
    "    cum_prob = np.linspace(0, 1., len(data))\n",
    "    ax1.plot(data, cum_prob,\n",
    "             '-', lw=2 \n",
    "            );\n",
    "ax1.set_xlabel('Error on translation (m)')\n",
    "ax1.set_ylabel('Cumulative probability')\n",
    "ax1.set_xlim([0., data.max()])\n",
    "ax1.set_ylim([0., 1.01])\n",
    "ax1.legend(solution_names)\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "for name in solution_names:\n",
    "    data = plot_data[name].error_rot.sort(inplace=False).values\n",
    "    cum_prob = np.linspace(0, 1., len(data))\n",
    "    ax2.plot(data, cum_prob,\n",
    "             '-', lw=2 \n",
    "            );\n",
    "\n",
    "ax2.set_xlabel('Error on rotation (rad)')\n",
    "ax2.set_ylabel('Cumulative probability')\n",
    "ax2.set_xlim([0., data.max()])\n",
    "ax2.set_ylim([0., 1.01])\n",
    "ax2.legend(solution_names)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g1JM6Xk91rtAifuOJVn2O8QRjTE/5sy9haiYmY553n6vqn8Hj869g7v8F2P4u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h3ZDBsBEhj02MPctyV9/El0z+1gl53MaP1CCDLKJ2wkweFojMZLLuwyuut2+P6TDjaAZw/rbri1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e392aa750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.hexbin(plot_data.validation.perturbation_trans.unique(), plot_data.validation.perturbation_rot.unique(), \n",
    "           gridsize=30);\n",
    "ax1.set_xlabel('Translation (m)')\n",
    "ax1.set_ylabel('Rotation (rad)')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W43Up8IHcuPJHamycVmPjWq/LwSJyi4+06MyZ9wK3bkEhj0v9WNIBSduNMb3G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\nyP8Pl+quvPNPwEoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e37d3ced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.violinplot(plot_data[[('point-to-point', 'error_trans'), ('point-to-plane', 'error_trans')]].values)\n",
    "ax1.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "point-to-point  error_rot    0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(plot_data[[('point-to-point', 'error_rot')]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.3344,  2.9928,  5.9257, ...,  3.8618,  2.7754,  2.2971])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data['point-to-point', 'time'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
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